Fluorescence microscopy, which employs fluorescent tags to label and observe cellular structures and their dynamics, is a powerful tool for life sciences. However, due to the spectral overlap between different dyes, a limited number of structures can be separately labeled and imaged for live-cell applications. In addition, the conventional sequential channel imaging procedure is quite time consuming, as it needs to switch either different lasers or filters. Here, we propose a novel double-structure network (DBSN) that consists of multiple connected models, which can extract six distinct subcellular structures from three raw images with only two separate fluorescent labels. DBSN combines the intensity-balance model to compensate for uneven fluorescent labels for different structures and the structure-separation model to extract multiple different structures with the same fluorescent labels. Therefore, DBSN breaks the bottleneck of the existing technologies and holds immense potential applications in the field of cell biology.

中文翻译：

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深度学习允许对具有相同荧光团的多个结构进行成像


荧光显微镜使用荧光标记来标记和观察细胞结构及其动力学，是生命科学的强大工具。然而，由于不同染料之间的光谱重叠，可以单独标记和成像有限数量的结构以用于活细胞应用。此外，传统的顺序通道成像程序非常耗时，因为它需要切换不同的激光器或滤光片。在这里，我们提出了一种由多个连接模型组成的新型双结构网络 （DBSN），它可以从三个原始图像中提取六个不同的亚细胞结构，只需两个单独的荧光标记。DBSN 结合了强度平衡模型来补偿不同结构的不均匀荧光标记，以及结构分离模型来提取具有相同荧光标记的多个不同结构。因此，DBSN 打破了现有技术的瓶颈，在细胞生物学领域具有巨大的潜在应用价值。